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Assess potential data breach impact by reviewing sensitive data and its movement through a codebase. Produce an inventory, exposure rankings, financial estimates, and prioritized safeguards, distinguishing legal source figures from planning estimates.
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Pre-breach impact analysis: inventories sensitive data (PII, PHI, PCI-DSS, credentials), traces data flows, scores exposure vectors, and produces a regulatory blast radius report with fine ranges sourced verbatim from GDPR Art. 83, CCPA § 1798.155(a), and HIPAA 45 CFR § 160.404. Cost benchmarks from IBM Cost of a Data Breach Report (annually updated). All citations in references/SOURCES.md for verification. Use when asked: "assess breach impact", "what data could be exposed", "calculate blast radius", "data exposure analysis", "how bad would a breach be", "quantify data risk", "sensitive data inventory", "data flow security audit", "pre-breach assessment", "worst-case breach scenario", "breach readiness", "data risk report", "/data-breach-blast-radius". For any stack handling user data, health records, or financial information. Output labels law-sourced figures (exact) vs heuristic estimates (planning only). Does not replace legal counsel.
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Plan controlled failure experiments to uncover system weaknesses. Define normal operating metrics, estimate affected users and reliability risk, set abort and recovery conditions, and turn experiment results into a structured review with follow-up actions.
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Assess how changes, failures, or paused updates to a BigQuery table or view affect downstream assets. Trace data lineage, the relationships showing where data flows, and summarize dependent tables, dashboards, and processes with direct and indirect impacts.
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Map sensitive data across the systems you manage, explore what a compromised asset could expose, and maintain a risk register. Record likelihood, impact, owners, mitigations, and review dates to support ongoing security planning.
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Build a graph of functions and calls across multiple programming languages to investigate entry points, paths to sensitive operations, and potential impact. Use the results to prioritize security reviews and connect external findings with source code.
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Analyze code structure with Trailmark to identify complex functions, trace input flow, assess impact across connected code, and locate privilege boundaries. The resulting JSON provides detailed evidence for deciding where to focus a code audit.
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Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', reviewing a small diff you don't trust, or a brief that asserts something about existing code ('make X public', 'X already handles Y') before you design against that assertion.
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Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.
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Run the worst-case drill before an agent goes autonomous — the 'if this agent were fully hijacked right now, what's the damage' walk-through, the containment controls (caps, kill-switch, reversibility, isolation), and the recovery plan. Use when asked what's the worst my agent could do, run a blast-radius assessment, prepare for an agent going rogue, or am I ready to let this run unattended. Produces the worst-case walk-through, the containment controls, the reversibility audit, and the incident-recovery runbook.
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Chaos engineering: hypothesis-driven fault injection to surface weakness before users do. Use when designing a chaos experiment, planning a gameday, choosing what to inject, computing blast radius, or building a chaos maturity model.
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Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.
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Pre-change blast-radius report for a symbol or file. Walks tree-sitting references, augments with a plain-text scan over non-parsed files (configs, plain docs), and clusters affected sites by feature (`_FEATURES.md`) or top-level package. Use when about to refactor, rename, or delete something in a repo you don't own — "what breaks if I change `validateUser`", "who calls this", "is this safe to remove", "where is this used", "blast radius", "impact analysis". This is the CONVERGENT pre-change risk skill — for "what is this repo?" use exploring-codebases; for "where is X?" use searching-codebases.
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Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.
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[Code Intelligence] Use when building/syncing the code graph, querying callers/imports/tests, tracing flow, checking blast radius, linking APIs or exporting JSON/Mermaid. --mode={build|query|trace|blast-radius|connect-api|export}.
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Find what a change could break somewhere else before it ships, beyond the diff, and prove the one fact it's safe because of by running real code instead of writing it up. Use for 'blast radius of X', 'what could this break', or reviewing a small diff you don't trust.
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Use when you need blast-radius analysis, dependency graphs, cross-repo impact, breaking-change diff, or architectural overview of .NET workspaces. Keywords: blast radius, dependency graph, impact analysis, cross-repo, call graph, endpoint map, EF Core lineage, breaking change, daemon, reindex.
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Web サーバ（nginx/apache、Linux 中心）の Phase 1 設定セキュリティレビュー。 target_profile.yaml で対象を確定し、MANIFEST.txt + manifest_attestation.txt で証跡 integrity を検証。 SSH 直接または証跡受領モードで 9 観点 (OS/リソース/ログ/ネットワーク/サービス/認証/監視/バックアップ/証明書) を read-only 走査。6 階層ガード (allowed/conditional/conditional_sensitive/ask-first/exceptional_sensitive_read/forbidden)、 3 軸補助 (Exploitability/Blast Radius/Service Criticality) の重大度判定、 observation_status による未確認の独立管理、evidence_dir/raw_evidence_store 分離、自己レビュー必須。 Windows/IIS は v1 hard fail。 Use when: Web サーバの設定セキュリティレビュー、Phase 1 監査、nginx/apache 構成監査、 EOL ランタイム調査、ログローテーション不備調査、ドキュメントルート漏えい調査。
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Map symbol dependencies, callers, and blast radius before editing code. Load when the user asks what depends on a symbol, what breaks if they change something, blast radius of a change, reverse dependencies, or which tests cover a function. Also triggers on "who calls this", "impact of changing", "dependency map", "what uses this", "find callers", or before any non-trivial edit when safe-change is not yet active. Pairs with codebase-understanding for broad architecture; this skill is symbol-scoped and edit-gated.
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Expert guide for in-memory Abstract Syntax Tree (AST) code knowledge graphs, real-time symbol dependency mapping, refactoring blast radius calculation, and dead code eradication / Panduan ahli graf pengetahuan AST kode in-memory, pemetaan dependensi simbol real-time, kalkulasi radius dampak refaktor, dan pembersihan kode mati.
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在修改代码、数据或API前，根据实际依赖关系整理影响范围及所需验证和恢复步骤，再与实现后的差异比对，检查遗漏、范围偏移和验证不足。
Investigate AWS production incidents using reusable CloudWatch log queries and metric calculations. Narrow affected resources, compare alarms with deployment events, and reconstruct a timeline to identify changes that may explain the symptoms.
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Design and configure VLANs, logical network partitions, to separate employee devices, servers, guests, and IoT equipment. Covers traffic restrictions between zones, switch security settings, and tests to verify that the intended isolation works.
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Review code changes against the previous commit and identify affected files and functions. Use dependency relationships to assess risk, check missing tests, and produce a focused report with issues and recommendations.
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